Increasingly, large companies face the challenge of storing, manipulating, and querying terabytes of graph-structured data for enterprise-critical applications. To cope with efficient graph data management at scale, a plethora of graph processing systems and algorithms has been proposed. These solutions, however, are typically ill-suited for online querying in business applications because they can aggravate system landscape integration and do not offer typical database features, such as transactional support and query optimization at run-time. Even worse, graph algorithms show a tremendous variety in structure and expressiveness caused by their domain-specific implementation and therefore can only be integrated into a database management system using custom coding.